Application of reinforcement learning for segmentation of transrectal ultrasound images

Abstract

BackgroundAmong different medical image modalities, ultrasound imaging has a very widespread clinical use. But, due to some factors, such as poor image contrast, noise and missing or diffuse boundaries, the ultrasound images are inherently difficult to segment. An important application is estimation of the location and volume of the prostate in transrectal ultrasound (TRUS) images. For this purpose, manual segmentation is a tedious and time consuming procedure.MethodsWe introduce a new method for the segmentation of the prostate in transrectal ultrasound images, using a reinforcement learning scheme. This algorithm is used to find the appropriate local values for sub-images and to extract the prostate. It contains an offline stage, where the reinforcement learning agent uses some images and manually segmented versions of these images to learn from. The reinforcement agent is provided with reward/punishment, determined objectively to explore/exploit the solution space. After this stage, the agent has acquired knowledge stored in the Q-matrix. The agent can then use this knowledge for new input images to extract a coarse version of the prostate.ResultsWe have carried out experiments to segment TRUS images. The results demonstrate the potential of this approach in the field of medical image segmentation.ConclusionBy using the proposed method, we can find the appropriate local values and segment the prostate. This approach can be used for segmentation tasks containing one object of interest. To improve this prototype, more investigations are needed.

Improving the quality of image segmentation in Ultrasound images using Reinforcement Learning

“…In the training phase, agent acts to learn appropriate parameters according to reinforcement learning method for hand segmented images [14]. At the end of training phase, a strategy table is obtained in which for each state, appropriate action is resulted, also the most appropriate sizes of sub-images and values of morphological operators for improving segmented image quality is determined.…”

“…In this stage, the values of actions which is determined by set δ [14]. Its members are pairs of intensity values τ 1 which are selected among the minimum value of available intensity in sub image with display and the maximum value of available intensity with display Operator radius value is shown by which is selected between arbitrary values 1 and .…”

“…The components of this vector include area, compression, position of sub image and the number of objects in image segmentation which according to [14] are respectively displayed by 1 …”

“…The image segmentation method using reinforcement learning with algorithm Q(λ) is implemented in [3]. In [14] and [15], reinforcement learning is used for segmentation of medical images after breaking it to a set of subimages. In [16], a method is employed in which threshold values for image segmentation are calculated from total weight threshold value in different various methods and reinforcement learning agent proceed to determine weights values dedicated to these values.…”

Reinforcement Learning Solution for Unit Commitment Problem through Pursuit Method

“…Reinforcement Learning based solutions have been proposed to several control and optimization tasks like playing Backgammon [2], robotics and control [3 -5], medical imaging [6] etc.…”